Advancement of an automatic segmentation pipeline for metallic artifact removal in post-surgical ACL MRI.
Authors
Affiliations (4)
Affiliations (4)
- Department of Orthopaedics, Warren Alpert Medical School of Brown University, Providence, RI, USA; Rhode Island Hospital, Providence, RI, USA.
- Department of Orthopaedics, Warren Alpert Medical School of Brown University, Providence, RI, USA; Brown University Health Biostatistics, Epidemiology, Research Design, & Informatics Core, Rhode Island Hospital, Providence, RI, USA.
- Department of Orthopaedic Surgery and Sports Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Orthopaedics, Warren Alpert Medical School of Brown University, Providence, RI, USA; Rhode Island Hospital, Providence, RI, USA. Electronic address: [email protected].
Abstract
Magnetic resonance imaging (MRI) has the potential to identify post-operative risk factors for re-tearing an anterior cruciate ligament (ACL) using a combination of imaging signal intensity (SI) and cross-sectional area measurements of the healing ACL. During surgery micro-debris can result from drilling the osseous tunnels for graft and/or suture insertion. The debris presents a limitation when using post-surgical MRI to assess reinjury risk as it causes rapid magnetic field variations during acquisition, leading to signal loss within a voxel. The present study demonstrates how K-means clustering can refine an automatic segmentation algorithm to remove the lost signal intensity values induced by the artifacts in the image. MRI data were obtained from 82 patients enrolled in three prospective clinical trials of ACL surgery. Constructive Interference in Steady State MRIs were collected at 6 months post-operation. Manual segmentation of the ACL with metallic artifacts removed served as the gold standard. The accuracy of the automatic ACL segmentations was compared using Dice coefficient, sensitivity, and precision. The performance of the automatic segmentation was comparable to manual segmentation (Dice coefficient = .81, precision = .81, sensitivity = .82). The normalized average signal intensity was calculated as 1.06 (±0.25) for the automatic and 1.04 (±0.23) for the manual segmentation, yielding a difference of 2%. These metrics emphasize the automatic segmentation model's ability to precisely capture ACL signal intensity while excluding artifact regions. The automatic artifact segmentation model described here could enhance qMRI's clinical utility by allowing for more accurate and time-efficient segmentations of the ACL.